An Integrated Affinity Propagation and Machine Learning Approach for Interference Management in Drone Base Stations

Li Chun Wang*, Yung Sheng Chao, Shao Hung Cheng, Zhu Han

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Drone small cells (DSCs) can provide on-demand air-to-ground wireless communications in various unexpected situations, such as traffic jam or natural disasters. However, a DSC needs to face the challenges such as severe co-channel interference, limited battery capacity, and fast topology changes. Aiming to improve energy efficiency of DSCs and quality of services of customers, this paper presents a learning-based multiple drone management (LDM) framework by controlling the transmission power and the 3-dimension location of DSCs based on location data, and reference signal received power of users. Since the labeled throughput data are typically not available in emergency situations, we develop unsupervised learning DSC management techniques: 1) affinity propagation interference management scheme to mitigate interference and energy consumption, and 2) K-means position adjustment to adjust the new 3-dimension positions of drones. Our numerical results show that the proposed LDM framework combining with affinity propagation clustering and k-means clustering can enhance the energy efficiency of DSCs by 25% and the signal-to-interference-plus-noise ratio of ground users by 56%, respectively.

Original languageEnglish
Pages (from-to)83-94
Number of pages12
JournalIEEE Transactions on Cognitive Communications and Networking
Volume6
Issue number1
DOIs
StatePublished - Mar 2020

Keywords

  • Base stations
  • Drones
  • Interchannel interference
  • interference reduction
  • machine leaning
  • Network topology
  • position management.
  • small cells
  • Throughput
  • Unmanned aerial vehicles
  • Unsupervised learning

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